





import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy





def  __magic_name__  (				lowercase				,       lowercase				)				-> List[Any]:

							"""simple docstring"""



							assert isinstance(lowercase				,       lowercase				)
							assert dataset.num_rows == 4
							assert dataset.num_columns == 3
							assert dataset.column_names == ["col_1", "col_2", "col_3"]
							for feature, expected_dtype in expected_features.items():
														assert dataset.features[feature].dtype == expected_dtype





@require_sqlalchemy
@pytest.mark.parametrize("""keep_in_memory"""				,       [False, True]				)
def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				,       lowercase				)				-> Any:

							"""simple docstring"""



							lowercase_	:   List[Any]  				=     tmp_path / """cache"""
							lowercase_	:   Optional[Any]  				=     {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														lowercase_	:   Tuple  				=     SqlDatasetReader(
														    """dataset"""				,       """sqlite:///""" + sqlite_path				,       cache_dir=lowercase				,       keep_in_memory=lowercase				).read()
							_check_sql_dataset(lowercase				,       lowercase				)





@require_sqlalchemy
@pytest.mark.parametrize(
    """features"""				,       [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]				,       )
def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				,       lowercase				)				-> Tuple:

							"""simple docstring"""



							lowercase_	:   Tuple  				=     tmp_path / """cache"""
							lowercase_	:   List[str]  				=     {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							lowercase_	:   Dict  				=     features.copy() if features else default_expected_features
							lowercase_	:   int  				=     (
							    Features({feature: Value(lowercase				) for feature, dtype in features.items()}				) if features is not None else None
							)
							lowercase_	:   Union[str, Any]  				=     SqlDatasetReader("""dataset"""				,       """sqlite:///""" + sqlite_path				,       features=lowercase				,       cache_dir=lowercase				).read()
							_check_sql_dataset(lowercase				,       lowercase				)





def  __magic_name__  (				lowercase				)				-> Any:

							"""simple docstring"""



							with contextlib.closing(sqlitea.connect(lowercase				)				) as con:
														lowercase_	:   Tuple  				=     con.cursor()
														cur.execute("""SELECT * FROM dataset"""				)
														for row in cur:
																					yield row





@require_sqlalchemy
def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> int:

							"""simple docstring"""



							lowercase_	:   Optional[Any]  				=     tmp_path / """cache"""
							lowercase_	:   int  				=     os.path.join(lowercase				,       """tmp.sql"""				)
							lowercase_	:   Any  				=     SqlDatasetReader("""dataset"""				,       """sqlite:///""" + sqlite_path				,       cache_dir=lowercase				).read()
							SqlDatasetWriter(lowercase				,       """dataset"""				,       """sqlite:///""" + output_sqlite_path				,       num_proc=1				).write()

							lowercase_	:   List[str]  				=     iter_sql_file(lowercase				)
							lowercase_	:   Dict  				=     iter_sql_file(lowercase				)

							for rowa, rowa in zip(lowercase				,       lowercase				):
														assert rowa == rowa





@require_sqlalchemy
def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> Optional[int]:

							"""simple docstring"""



							lowercase_	:   Any  				=     tmp_path / """cache"""
							lowercase_	:   Optional[Any]  				=     os.path.join(lowercase				,       """tmp.sql"""				)
							lowercase_	:   List[Any]  				=     SqlDatasetReader("""dataset"""				,       """sqlite:///""" + sqlite_path				,       cache_dir=lowercase				).read()
							SqlDatasetWriter(lowercase				,       """dataset"""				,       """sqlite:///""" + output_sqlite_path				,       num_proc=2				).write()

							lowercase_	:   Any  				=     iter_sql_file(lowercase				)
							lowercase_	:   Tuple  				=     iter_sql_file(lowercase				)

							for rowa, rowa in zip(lowercase				,       lowercase				):
														assert rowa == rowa





@require_sqlalchemy
def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> Dict:

							"""simple docstring"""



							lowercase_	:   Dict  				=     tmp_path / """cache"""
							lowercase_	:   List[str]  				=     os.path.join(lowercase				,       """tmp.sql"""				)
							lowercase_	:   int  				=     SqlDatasetReader("""dataset"""				,       """sqlite:///""" + sqlite_path				,       cache_dir=lowercase				).read()
							with pytest.raises(lowercase				):
														SqlDatasetWriter(lowercase				,       """dataset"""				,       """sqlite:///""" + output_sqlite_path				,       num_proc=0				).write()
"""simple docstring"""



import os

import pytest
from attr import dataclass


SCREAMING_SNAKE_CASE__:List[str]									=			"""us-east-1"""  # defaults region






@dataclass
class  snake_case__      :
 _snake_case       :					str
 _snake_case       :					Optional[Any]							  =						"""arn:aws:iam::558105141721:role/sagemaker_execution_role"""
 _snake_case       :					Optional[Any]							  =						{
     """task_name""": """mnli""",
     """per_device_train_batch_size""": 16,
     """per_device_eval_batch_size""": 16,
     """do_train""": True,
     """do_eval""": True,
     """do_predict""": True,
     """output_dir""": """/opt/ml/model""",
     """overwrite_output_dir""": True,
     """max_steps""": 500,
     """save_steps""": 5_500,
 }
 _snake_case       :					List[str]							  =						{**hyperparameters, """max_steps""": 1_000}


 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
                   {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
               ]
        else:
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
                   {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
               ]


 @property
 def 						a__   ( self						):
        return F"{self.framework}-transfromers-test"


 @property
 def 						a__   ( self						):
        return F"./tests/sagemaker/scripts/{self.framework}"






 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
        else:
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"



@pytest.fixture(scope="class"				)
def 	_lowerCamelCase(	a				):
       __a       =    SageMakerTestEnvironment(framework=request.cls.framework				)
